Abstract:

DESCRIPTION (provided by applicant): Simultaneously recorded EEG/fMRI measures of higher order cognitive processing in the brain during complex task performance have been difficult to establish. The problems are due to the difficulty of collecting the EEG in a high-gradient field and the complexity of the cortical networks undedying human cognition. To shed light on the nature of executive information processing in the human brain, researchers have investigated the dynamics of inter- and intracortical networks using state-of-the-art anatomical, hemodynamic, and electroencephalographic (EEG) neuroimaging modalities. However, there are significant difficulties in acquiring artifact free data while inside an operating functional Magnetic Resonance Imaging (fMRI) system.
Although, there are commercial products on the market for recording EEG in the magnet, these systems are limited in that they record EEG epochs interleaved between echo-planar imaging sequences. Hence, researchers are unable to simultaneously identify precise areas or networks in the brain responsible for the cognitive activation seen in either modality, and, therefore, cannot cross-validate their results. Our Phase I Improved Multimodal Acquisition and Processing System (IMAPS) proposal, describes innovative methods to simultaneously record EEG and fMRI during echo-planar imaging (EPi) sequences.
We have found that the most critical issue in EEG neuroimaging today is the acquisition of uncontaminated data; hence, our proposed feasibility study would focus on identifying the sensor interface conditions necessary to rapidly record robust low-impedance EEG signals from inside an operating fMRI. Then, we intend to use these data to guide the development of an innovative tacky-gel electrolyte to minimize the application time and eliminate the discomfort of manually abrading the skin at each electrode site necessary to reduce input impedance. Additionally, in Phase I, we would test the effectiveness of an enhanced sensor montage to diminish unwanted artifacts induced at the sensor interface. The goal of our Phase I effort would be to ascertain if EEG signals could be recorded during simultaneous fMRI operation that provided sufficient signal strength to allow post hoc artifact removal and EEG signal enhancement. If feasible, in Phase II, we would investigate the development of automated EP! artifact removal and EEG signal enhancement algorithms specific to the fMRI environment.
Our commercialization strategy would market an innovative sensor system and fMRI/EEG artifact removal toolkit allowing neuroimaging researchers to cross-validate electrical and hemodynamic activation areas responsible for normal and impaired cognitive function.